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Longitudinal characteristics of lymphocyte responses and cytokine profiles in the
peripheral blood of SARS-CoV-2 infected patients
Jing Liu1,3* , Sumeng Li1,3*, Jia Liu1,3*; Boyun Liang1,3, Xiaobei Wang4, Hua Wang1,
Wei Li1.3, Qiaoxia Tong1.3, Jianhua Yi1.3, Lei Zhao1.3, Lijuan Xiong1.3, Chunxia Guo1.3,
Jin Tian1.3, Jinzhuo Luo1.3, Jinghong Yao1.3, Ran Pang1.3,Hui Shen1.3, Cheng Peng1.3,
Ting Liu1.3, Qian Zhang1.3, Jun Wu1.3, Ling Xu1.3, Sihong Lu1.3, Baoju Wang1.3,
Zhihong Weng1.3, Chunrong Han1.3, Huabing Zhu1.3, Ruxia Zhou1.3, Helong Zhou1.3,
Xiliu Chen1.3, Pian Ye1.3, Bin Zhu1.3, Shengsong He1.3, Yongwen He1.3, Shenghua Jie1.3,
Ping Wei1.3, Jianao Zhang1.3, Yinping Lu1.3,Weixian Wang1.3, Li Zhang1.3, Ling Li1.3,
Fengqin Zhou1.3, Jun Wang2,3, Ulf Dittmer2,3, Mengji Lu2,3 Yu Hu5#, Dongliang
Yang1.3#, Xin Zheng1.3#
1 Department of Infectious Diseases, Union Hospital, Tongji Medical College,
Huazhong University of Science and Technology, Wuhan 430022, China
2 Institute for Virology, University Hospital of Essen, University of Duisburg-Essen,
Essen 45147, Germany
3 Joint International Laboratory of Infection and Immunity, Huazhong University of
Science and Technology, Wuhan 430022, China
4 Department of Clinical Laboratory, Union Hospital, Tongji Medical College,
Huazhong University of Science and Technology, Wuhan 430022, China
5 Department of Hematology, Union Hospital, Tongji Medical College, Huazhong
University of Science and Technology, Wuhan 430022, China
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2
* # These authors contribute equally to this work.
Correspondence to:
Prof. Dr. Yu Hu,
E-mail: [email protected], Tel:+86 27 85726301
Department of Hematology, Union Hospital, Tongji Medical College, Huazhong
University of Science and Technology, Wuhan 430022, China
Prof. Dr. Dongliang Yang,
E-mail: [email protected], Tel: +86 27 85726130
Department of Infectious Diseases, Union Hospital, Tongji Medical College,
Huazhong University of Science and Technology, Wuhan 430022, China
Prof. Dr. Xin Zheng,
E-mail: [email protected], Tel: +86 27 85726732
Department of Infectious Diseases, Union Hospital, Tongji Medical College,
Huazhong University of Science and Technology, Wuhan 430022, China
Financial support:
This work is supported by the National Natural Science Foundation of China
(81861138044, 91742114 and 91642118), the National Science and Technology Major
Project (2018ZX10723203, 2018ZX10302206, 2017ZX10202201, 2017ZX10202202
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3
and 2017ZX10202203), the Innovation Team Project of Health Commission of Hubei
Province (WJ2019C003), the Integrated Innovative Team for Major Human Diseases
Program of Tongji Medical College and the “Double-First Class” Project for the
International Cooperation Center on Infection and Immunity, HUST, and a special
joint project of University Hospital Essen, University of Duisburg-Essen.
Conflict of interest:
The authors disclose no conflicts of interest.
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Abstract
Background: The dynamic changes of lymphocyte subsets and cytokines profiles of
patients with novel coronavirus disease (COVID-19) and their correlation with the
disease severity remain unclear.
Method: Peripheral blood samples were longitudinally collected from 40 confirmed
COVID-19 patients and examined for lymphocyte subsets by flow cytometry and
cytokine profiles by specific immunoassays.
Findings: Of the 40 COVID-19 patients enrolled, 13 severe cases showed significant
and sustained decreases in lymphocyte counts but increases in neutrophil counts than
27 mild cases. Further analysis demonstrated significant decreases in the counts of T
cells, especially CD8 + T cells, as well as increases in IL-6, IL-10, IL-2 and IFN-γ
levels in the peripheral blood in the severe cases compared to those in the mild cases.
T cell counts and cytokine levels in severe COVID-19 patients who survived the
disease gradually recovered at later time points to levels that were comparable to
those of the mild cases. Moreover, the neutrophil-to-CD8+ T cell ratio (N8R) were
identified as the most powerful prognostic factor affecting the prognosis for severe
COVID-19.
Conclusion: The degree of lymphopenia and a proinflammatory cytokine storm is
higher in severe COVID-19 patients than in mild cases, and is associated with the
disease severity. N8R may serve as a useful prognostic factor for early identification
of severe COVID-19 cases.
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5
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6
Introduction
First reported in Wuhan, China, on 31 December 2019, an ongoing outbreak of a viral
pneumonia in humans has raised acute and grave global concern. The causative
pathogen was rapidly identified as a novel β-coronavirus, which has since been
formally named as the severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) by the International Committee on Taxonomy of Viruses. According
to the daily report of the National Health Commission of China, the epidemic of
SARS-CoV-2 has so far caused 57,416 confirmed cases, including 11,272 severe
cases, and 1,665 deaths in China by February 15th, 2020.1 The disease caused by
SARS-CoV-2 has been recently named as the Coronavirus Disease-2019 (COVID-19)
by the World Health Organization. Previous studies about the epidemiological and
clinical characteristics of COVID-19 showed patients with COVID-19 may develop
either mild or severe symptoms of acute respiratory infection, while the mild patients
show symptoms of fever, dry cough, fatigue, abnormal chest CT findings but with a
good prognosis.2-3 In contrast, some patients develop severe pneumonia, acute
respiratory distress syndrome (ARDS) or multiple organ failure, with death rates
ranging from between 4.3% to 15% according to different study reports.2, 4
Lymphopenia and inflammatory cytokine storm are typical laboratory abnormalities
observed during highly pathogenic coronavirus infections, such as the severe acute
respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory
syndrome coronavirus (MERS-CoV) infections, and are believed to be associated
with disease severities.5-6 Recent studies have also reported decreases in the counts of
lymphocytes in the peripheral blood and increases in serum inflammatory cytokine
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7
levels in COVID-19 patients.4, 7 However, it has remained largely unclear how
different lymphocyte subsets and the kinetics of inflammatory cytokines change in the
peripheral blood during COVID-19. In this study, we longitudinally characterized the
changes of lymphocyte subsets and cytokines profiles in the peripheral blood of
COVID-19 patients with distinct disease severities.
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8
Methods
Data collection
A written informed consent was regularly obtained from all patients upon admission
into Wuhan Union Hospital, China. The study was approved by the Ethics Committee
of Tongji Medical College of Huazhong University of Science and Technology. The
40 confirmed COVID-19 patients at Wuhan Union Hospital during January 5 to
January 24, 2020 were enrolled into this retrospective single-center study. All medical
record information including epidemiological, demographic, clinical manifestation,
laboratory data, and outcome data were obtained. All data were checked by a team of
trained physicians.
Laboratory examination
Laboratory confirmation of the SARS-CoV-2 was performed by local CDC according
to Chinese CDC protocol. Throat-swab specimens were collected from all patients
and the samples were maintained in viral-transport medium for laboratory testing. An
infection with other respiratory viruses including influenza A virus, influenza B virus,
Coxsackie virus, respiratory syncytial virus, parainfluenza virus and enterovirus was
excluded by real-time RT-PCR. Specimens, including sputum or alveolar lavatory
fluid, blood, urine, and feces, were cultured to identify pathogenic bacteria or fungi
that may be associated with the SARS-CoV-2 infection. The specific IgG and IgM of
Chlamydia pneumonia and Mycoplasma pneumonia were detected by
chemiluminescence immunoassay. The lymphocyte test kit (Beckman Coulter Inc., FL,
USA) was used for lymphocyte subset analysis. Plasma cytokines (IL2, IL4, IL6,
IL10, TNF - α and IFN - γ) were detected with human Th1/2 cytokine kit II (BD Ltd.,
Franklin lakes, NJ, USA). All tests are performed according to the product manual.
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9
Statistical analyses
Classification variables are expressed in frequency or percentage, and significance is
detected by chi square or Fisher’s exact test. The quantized variables of parameters
are expressed as mean ± standard deviation, and the significance is tested by t-test.
Nonparametric variables were expressed in median and quartile intervals, and
significance was tested by Mann Whitney U or Kruskal Wallis test. P < 0.05 was
considered statistically significant in all statistical analyses. Principal component
analysis (PCA) was performed to identify the major contributing factors among
clinical parameters to distinguish mild and severe cases of COVID-19 patients. The
diagnostic values of selected parameters for differentiating mild and severe cases of
COVID-19 patients were assessed by receiver operating characteristic (ROC) and area
under the ROC curve (AUC). SPSS statistical software (Macintosh version 26.0, IBM,
Armonk, NY, USA) and R package are used for statistical analysis.
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10
Results
Demographic and clinical characteristics of COVID-19 patients
The diagnosis of COVID-19 for patients was performed according to the Guidelines
of the Diagnosis and Treatment of New Coronavirus Pneumonia (version 5) published
by the National Health Commission of China. Mild patients met all following
conditions: (1) Epidemiology history, (2) Fever or other respiratory symptoms, (3)
Typical CT image abnormities of viral pneumonia, and (4) Positive result of RT-PCR
for SARS-CoV-2 RNA. Severe patients additionally met at least one of the following
conditions: (1) Shortness of breath, RR≥30 times/min, (2) Oxygen saturation (Resting
state) ≤93%, or (3) PaO2 / FiO2 ≤300mmHg. A total of 40 patients were enrolled in
this study, which were all Wuhan residents and laboratory confirmed cases. The
patients were divided into two groups according to above-mentioned conditions,
including 27 mild cases (67.5%) and 13 severe cases (32.5%). Two patients in the
severe group died on day 15 and 21 after disease onset.
The enrolled COVID-19 patients consisted of 15 males (37.5%) and 25 females
(62.5%) (Table 1). Only 3 patients (7.5%) had an exposure history (shopping) on the
Huanan seafood market in Wuhan. The medium age of the patients was 48.7 ± 13.9
years old. The ages of the severe patient group (59.7 ± 10.1 years) were older than
that of the mild group (43.2 ± 12.3 years). A total of 14 (35%) patients in both groups
had basic diseases, including diabetes (6 [15%]), hypertension (6 [15%]), pituitary
adenoma (2 [5%]), thyroid disease (2 [5%]) and tumor disease (2 [5%]). Four severe
patients had mixed fungal infection and 1 severe patient had mixed bacterial infection
(Table 1). All severe patients and 85.2% of the mild patients had fever, while no
significant difference in the degrees of temperature was observed between the two
groups (Table 1). The severe patients showed significantly higher frequencies in the
occurrence of sputum production (P=0.032), myalgia (p=0.041) and nausea (p=0.029)
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11
(Table 1). The levels of fibrinogen (p<0.001), D-dimer (P=0.008), total bilirubin
(P=0.007), aspartate transaminase (p<0.001), alanine transaminase (P=0.004), lactate
dehydrogenase (p<0.001), creatine kinase (P=0.010), C-reactive protein (P=0.006),
ferritin (P=0.015) and serum amyloid A protein (SAA, P=0.003) in the peripheral
blood of the severe patients were significantly higher at admission compared to the
mild patients (Table 2). No significant differences in the serum levels of
immunoglobulins (IgA, IgG and IgM), complement C3 or C4 were observed between
the two groups (Table 2).
Kinetic analysis of lymphocyte subsets in the peripheral blood of COVID-19
patients
Lymphopenia was observed in 44.4% (12/27) of mild patients and 84.6% (11/13) of
severe patients at the onset of the disease. As shown in Table 2, the absolute counts of
lymphocytes in the peripheral blood of the severe patients was significantly lower,
while the absolute counts of total white blood cells (WBCs) and neutrophils were
significantly higher, than those of the mild patients at the time of hospital admission.
No significant difference in monocyte counts was observed between the two groups
(Table 2). Next, we analyzed the kinetic changes of WBCs, neutrophils and
monocytes as well as different lymphocyte subsets in the peripheral blood of
COVID-19 patients from the disease onset to at least 16 days later. The two
mortalities in the severe group were excluded from the analysis due to the lack of
kinetic data. Significant increases in total WBCs counts in the severe group were only
observed at the time point of onset (within 3 days) but not during the following period
of disease progression compared to the mild group (Figure 1A). Significant increases
in neutrophil counts of the severe group were observed not only at the time point of
disease onset, but also at 13-15 days after compared to the mild group (Figure 1B). In
contrast, a sustained decrease in lymphocyte counts of the severe group was observed
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12
compared to those of the mild patients. The difference was significant at the time
point of disease onset and became even greater on 4-6 days later (Figure 1C). From
7-15 days after disease onset, the lymphocyte counts gradually increased in the severe
group, and reached a comparable level to that of the mild patients at 16 days after
disease onset (Figure 1C). No significant differences in monocyte counts were
observed between the two groups during the whole observation period (Figure 1D).
In order to further determine the kinetic changes of different lymphocyte subsets in
the peripheral blood of COVID-19 patients, we performed flow cytometry to stain
CD3+ T cells, CD4+ and CD8+ T cell subsets, B cells and NK cells. Similar to the
findings for lymphocytes, sustained decreases in CD3, CD8 and CD4 T cell counts
was observed in the severe group compared to those of the mild patients during
clinical observation (Figure 2A-C, Supplementary figure 1). The lowest CD3, CD4
and CD8 T cell counts were observed at 4-6 days after disease onset (Figure 2A-C).
The differences in CD3 and CD8 T cell counts between the two groups were
significant at the time points of disease onset and 7-9 days later (Figure 2A and 2C).
However, the differences in CD4 T cell counts between the two groups did not reach a
statistical significance at any time point (Figure 2C). The T cell counts started to
gradually increase in the severe group starting at 7 days after disease onset, and
reached comparable levels to those in the mild patients on day 16 after disease onset
(Figure 2A-C). No significant differences in B cell and NK cell counts were observed
between the two groups during the whole course of the disease (Figure 2D and 2E).
Kinetic analysis of inflammatory cytokine levels in the serum of COVID-19
patients
A previous study demonstrated changes in inflammatory cytokine levels, such as IL-2,
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IL-7, IL-10, and TNF-α, in the serum of COVID-19 patients.2 Therefore, we further
characterized the kinetic changes of inflammatory cytokine levels, including IL-2,
IL-4, IL-6, IL-10, IFN-γ and TNF-α, in the serum of our patient cohort. Fluctuations
in the serum levels of these cytokines in the mild patient group were minor. In
contrast, the severe patient group showed more significant fluctuations in the serum
levels of these cytokines (Figure 3). All examined cytokines, except IL-6, reached
their peak levels in the serum at 3-6 days after disease onset (Figure 3). Both IL-6 and
IL-10 levels showed sustained increases in the severe group compared to the mild
group (Figure 3A and 3B). A decease in serum IL-6 levels in the severe group started
at 16 days after disease onset, and IL-10 levels were lowest at 13 days after disease
onset (Figure 3A and 3B). Significant increases in serum IL-2 and IFN-γ levels in the
severe group were only observed at 4-6 days after disease onset (Figure 3C and 3F).
No significant differences in IL4 and TNF-α levels were observed between the two
groups during the whole course of the disease (Figure 3D and 3E). All examined
cytokines reached similar levels between the severe and mild patient groups at 16
days after disease onset (Figure 3).
Prognostic factors for identification of severe COVID-19 cases
Next, we examined the possibilities of using above-mentioned parameters as
prognostic factors for identifying severe cases in COVID-19 patients. PCA was firstly
performed by R package “factoextra” to identify correlated variables for
distinguishing severe patients from mild patients (Figure 4A). Four mostly
contributing variables, neutrophil-to-CD8+ T cell ratio (N8R),
neutrophil-to-lymphocyte ratio (NLR), neutrophil counts (NEC) and White Blood
Cells counts (WBCC) were selected as potential prognostic factors for further detailed
statistical analysis. To assess the diagnostic value of these 4 selected parameters,
receiver operating characteristic (ROC) curve and area under ROC curve (AUC) were
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14
calculated by R package “pROC” (Figure 4B). The results of this analysis identified
N8R with a higher AUC (0.94) than NLR (0.93), NE (0.91) and WBC (0.85).
Simultaneously, the cutoff values were calculated from the ROC curves, with a value
of 21.9 for N8R (Specificity: 92.6%, Sensitivity: 84.6%), 5.0 for NLR (96.3%,
84.6%), 3.2 for NE (81.5%, 84.6%) and 4.3 for WBC (74.1%, 84.6%) (Figure 4B).
The further logistic regression analysis including the 4 factors of N8R>21.9,
NLR>5.0, NE>3.2 and WBC>4.3 which were used to calculate odds ratios (ORs).
The results were obtained for NLR (OR: 143, 95% Cl: 11.72-1745.3), N8R (OR:
68.75, 95% Cl: 8.55-552.68), NE (OR: 22, 95% Cl: 3.646-132.735) and WBC (OR:
55, 95% Cl: 6.779-446.23) with our patient cohort as predictive factors for severe
COVID-19.
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15
Discussion
In this study, we analyzed the clinical features and immunological characteristics of
peripheral blood in patients with COVID-19. Although the majority of the patients did
not have an exposure history of the Huanan seafood market in Wuhan, the clinical
characteristics of these patients are very similar to those reported in previous
studies.2,4,7 The ages of severe patients are older, and the proportion of underlying
diseases is higher, and co-infection also occurs in severe patients. Recent reports show
that the lymphocyte counts are normal in COVID-19 patients with mild diseases. In
contrast, 63%-70.3% of patients with severe diseases have lymphopenia and the
lymphocyte counts in patients with a mortal outcome remain at a low level.4, 8 Our
study also confirmed higher rates of developing lymphopenia in severe patients than
in mild patients (84.6% vs 44.4%). We found that the development of lymphopenia in
severe patients was mainly related to the significantly decreased absolute counts of T
cells, especially CD8+ T cells, but not to B cells and NK cells. The decrease of T cells
in the severe patient group reaches its peak within the first week during the disease
course, and then T cell numbers gradually increase from the second week and recover
to a comparable level to that of the mild patient group in the third week. All these
severe patients included in our study survived the disease, and thus we speculate this
course is associated with a favorable outcome in severe COVID-19 patients.
Previous researches on SARS-CoV and MERS-CoV infections have demonstrated the
correlation between T cell counts and the severity of the diseases, as well as explored
the possible mechanisms.9 It has been shown that the acute SARS-CoV infection was
associated with marked lymphopenia in about 80% of patients, including a dramatic
loss of both CD4+ and CD8+ T cells in comparison with healthy control
individuals.10-12 Decreases in T cell numbers are strongly correlated with the severity
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16
of acute phase of SARS disease in patients.11, 13 Lymphopenia is also observed in
MERS-CoV infected patients. A detailed clinical study showed that 14 % of MERS
patients had leukopenia, while 34 % of the patients had lymphopenia.14 The
mechanism of developing lymphopenia may differ in SARS-CoV and MERS-CoV
infections. SARS-CoV cannot productively infect T cells, however, altered antigen
presenting cells (APC) function and impaired dendritic cells migration during
SARS-CoV infection may result in insufficient T cell priming and thus contribute to
decreased numbers of virus-specific T cells in the lungs.15-16 Moreover, delayed type I
interferon response or inflammatory monocyte-macrophages derived
pro-inflammatory cytokines could also sensitize T cells to undergo apoptosis.17 In
contrast, MERS-CoV was found to be able to infect many human immune cells,
including dendritic cells,18 macrophages,19 and T cells.20 MERS-CoV infection of T
cells results in apoptosis mediated by a combination of extrinsic and intrinsic
apoptosis pathways, which is believed to contribute to virus spread and the severe
immunopathology.20 So far, it remains unclear whether SARS-CoV-2 induced T cell
contraction is the result of a direct T cell infection or an indirect effect cause by the
virus, such as APC function disorder or overactive inflammatory responses. Further
studies are needed to investigate the corresponding mechanisms in detail.
Previous studies have shown that elevated levels of proinflammatory cytokines, such
as IFN-γ, TNF-a, IL-6 and IL-8, are associated with severe lung injury and adverse
outcomes of SARS-CoV or MERS-CoV infection.6, 18-19, 21 Our results also
demonstrate that severe COVID-19 patients have higher concentrations of IL6, IL10,
IL2 and IFN-γ in the serum than mild cases, suggesting that the magnitude of
cytokine storm is associated with the disease severity. Additionally, T cells are
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17
important for dampening overactive innate immune responses during viral
infection.22-23 Thus, loss of T cells during SARS-CoV-2 infection may result in
aggravated inflammatory responses, while restoring T cell numbers may alleviate
them. In line with this hypothesis, we observed that the kinetic changes of T cell
counts are reversely correlated with the kinetic changes of most examined cytokine
levels in the peripheral blood in severe COVID-19 patients. While T cell counts drop
to their lowest levels at 4-6 days after disease onset, serum IL-10, IL-2, IL-4, TNF-α
and IFN-γ levels reach their peaks. The courses of restoring T cell numbers are
associated with decreases of serum IL-6, IL-10, IL-2, IL-4, TNF-α and IFN-γ levels.
Early identification of risk factors for severe COVID-19 patients may facilitate
appropriate supportive care and promptly access to the intensive care unit if necessary.
A recent study in a 61-patient cohort [Liu J, et al. medRxiv. 2020] reported that the
NLR was the most useful prognostic factor affecting the prognosis for severe
COVID-19. The severity of pathological injury during SARS or MERS correlates
with the extensive infiltration of neutrophils in the lung and increased neutrophil
numbers in the peripheral blood.17 Thus, the magnitude of increase in neutrophil
counts may suggest the intensity of inflammatory responses in COVID-19 patients.
Besides, the magnitude of decrease in lymphocyte counts also indicates the extend of
the impairment of immune system by the viral infection. Therefore, NLR may serve
as a useful factor to reflect the intensity of imbalance of inflammation and immune
responses in COVID-19 patients. In this study, we also screened the potential
prognostic factors affecting incidence of severe illness in our patient cohort. Based on
our findings with analyzing lymphocyte subsets, we further included the ratio of
neutrophils to different lymphocyte subsets as parameters. Our kinetic analysis
revealed that CD8+ T cells are the major lymphocyte subset which decreases in cell
numbers during COVID-19. In line with this finding, our results demonstrate that
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18
N8R has even better performance with a higher AUC value than NLR in the ROC
curve analysis, and may serve as a more powerful factor than NLR for predicting the
severe illness incidence in COVID patients.
In summary, our study of immunological characteristics of the peripheral blood in
COVID-19 patients shows that the numbers of neutrophils and T cells, especially
CD8+ T cells, as well as the levels of inflammatory cytokines in the peripheral blood
is dynamically correlated with the severity of the disease. To the best of our
knowledge, this is the first work to describe the kinetic changes of lymphocyte subsets
and cytokine profiles in COVID-19 patients. Importantly, we identified N8R and NLR
as powerful prognostic factors for early identification of severe COVID-19 cases.
This work may help to achieve a better understanding of immune function disorder as
well as immunopathogenesis during SARS-CoV-2 infection.
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19
Acknowledgement
We thank all the doctors, nurses, disease control workers, and researchers who have
fought bravely and ceaseless against the virus on the frontline during the
SARS-CoV-2 epidemic, some of whom lost their lives in doing so. We thank those
who have given great and selfless support to the fight against the virus. We thank Ms.
Delia Cosgrove and Ms. Ursula Schrammel for language correction of this
manuscript.
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20
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2. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019
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Table 1. Demographics and baseline characteristics of patients infected with
SARS-CoV-2.
Baseline variables All patients
(N=40)
Mild patients
(N=27)
Severe patients
(N=13)
P-value
Characteristics
Age (year) 48.7 ± 13.9 43.2 ± 12.3 59.7 ± 10.1 <0.001
Gender (%) 0.138
Men 15 (37.5) 8 (29.6) 7 (53.8)
Women 25 (62.5) 19 (70.4) 6 (46.2)
Huanan seafood market
exposure (%)
3 (7.5) 1 (3.7) 2 (5.4) 0.242
Underlying diseases (%) 14 (35.0) 7 (25.9) 7 (53.8) 0.155
Diabetes 6 (15.0) 2 (7.4) 4 (30.8) 0.075
Hypertension 6 (15.0) 1 (3.7) 5 (38.5) 0.010
Pituitary adenoma 2 (5.0) 1 (3.7) 1 (7.7) >0.999
Thyroid disease 2 (5.0) 2 (7.4) 0 >0.999
Malignancy 2 (5.0) 2 (7.4) 0 >0.999
Co-infection (%) 5 (12.5) 0 5 (38.5) 0.002
Fungi 4 (10.0) 0 4 (30.8) 0.008
Bacteria 1 (2.5) 0 1 (7.7) >0.999
Signs and symptoms
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Fever 36(90.0) 23(85.2) 13(100)
0.284
Highest temperature, °C
<37.3 4(10.0) 4(14.8)
0 0.284
37.3–38.0 10(25) 8(29.6) 2(15.4)
0.451
38.1–39.0 17(42.5) 9(33.3) 8(61.5)
0.091
>39.0 9(22.5) 6(22.2) 3(23.1)
>0.999
Chill 10(25) 5(18.5) 5(38.5)
0.246
Shivering 5(12.5) 2(7.4) 3(23.1)
0.307
Fatigue 22(55) 14(51.9) 8(61.5)
0.564
Cough 33(82.5) 22(81.5) 11(84.6)
>0.999
Sputum production 21(52.5) 11(40.7) 10(76.9)
0.032
Pharyngalgia 5(12.5) 4(14.8) 1(7.7)
>0.999
Dizziness 7(17.5) 4(14.8) 3(23.1)
0.662
Headache 8(20.0) 6(22.2) 2(15.4)
>0.999
Rhinorrhea 1(2.5) 1(3.7)
0 >0.999
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Chest tightness 12(30.0) 7(25.9) 5(38.5)
0.476
Chest pain 1(2.5) 1(3.7)
0 >0.999
Shortness of breath 5(12.5) 5(18.5)
0 0.154
Dyspnoea 1(2.5) 1(3.7)
0 >0.999
Myalgia 15(37.5) 7(25.9) 8(61.5)
0.041
Abdominal pain 1(2.5) 1(3.7)
0 >0.999
Diarrhoea 3(7.5) 1(3.7) 2(15.4)
0.242
Nausea 3(7.5)
0 3(23.1)
0.029
Vomiting 1(2.5)
0 1(7.7)
0.325
Hypoleucocytosis 10(25.0) 8(29.6) 2(15.4)
0.451
Lymphopenia 21(52.5) 11(40.7) 10(76.9)
0.046
Thrombocytopenia 5(12.5) 3(11.1) 2(15.4)
>0.999
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Table 2. Comparison of laboratory parameters between mild and severe 1
COVID-19 patients. 2
Baseline variables All patients
(N=40)
Mild patients
(N=27)
Severe patients
(N=13) P-value
Hemoglobin (g/l) 126.4 ± 13.4 127.8 ± 13.1 123.4 ± 14.0 0.334
Platelet (×109/L) 183.1 ± 69.0 181.4 ± 70.7 186.6 ± 68.1 0.826
White blood cell
(×109/L) 4.8 ± 2.6 3.9 ± 1.5 6.6 ± 3.4 0.002
Neutrophil (×109/L) 2.8 (1.6-4.3) 2.0 (1.5-2.9) 4.7 (3.6-5.8) <0.001
Lymphocyte (×109/L) 0.9 (0.7-1.3) 1.1 (0.8-1.4) 0.6 (0.6-0.8) 0.002
Monocyte (×109/L) 0.3 (0.2-0.5) 0.3 (0.2-0.5) 0.2 (0.2-0.5) 0.477
TBil (umol/l) 10.3 ± 5.0 8.8 ± 4.1 13.2 ± 5.5 0.007
ALT (U/L) 22.5 (16.8-31.2) 19.0 (13.5-26.0) 27.0 (23.0-50.0) 0.004
AST (U/L) 34.1 ± 17.7 25.9 ± 9.5 51.2 ± 18.7 <0.001
LDH (U/L) 303.9 ± 168.8 221.5 ± 71.2 462.4 ± 190.6 <0.001
CK (U/L) 59.5 (45.0-88.8) 51.0 (45.0-68.0) 104.0
(77.0-124.0) 0.010
Blood urea nitrogen
(mmol/l) 3.2 (2.5-4.3) 3.2 (2.5-4.4) 3.3 (2.7-3.7) 0.707
Serum creatinine (umol/l) 67.3 ± 19.7 64.0 ± 13.3 74.2 ± 28.3 0.128
Blood potassium (mmol/l) 3.8 ± 0.5 3.9 ± 0.5 3.7 ± 0.4 0.242
Blood sodium (mmol/l) 145.9 ± 43.4 149.5 ± 52.5 138.6 ± 6.2 0.462
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3
4
5
D-Dimer (mg/l) 0.6 (0.3-0.9) 0.4 (0.2-0.8) 0.9 (0.7-1.5) 0.008
PT (s) 13.2 ± 0.6 13.1 ± 0.6 13.4 ± 0.6 0.154
APTT (s) 39.5 ± 4.5 39.5 ± 4.6 39.5 ± 4.2 0.968
INR 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 0.154
FIB (g/l) 5.1 ± 1.6 4.5 ± 1.4 6.3 ± 1.3 <0.001
IgE 43.9 (14.4-98.0) 26.5 (12.8-76.1) 43.9
(27.0-105.5) 0.243
IgG 11.1 ± 2.0 10.8 ± 2.0 11.5 ± 2.0 0.370
IgA 2.2 ± 0.7 2.2 ± 0.8 2.4 ± 0.6 0.483
IgM 1.1 ± 0.4 1.1 ± 0.5 1.1 ± 0.3 0.918
C-reactive protein (mg/l) 38.1 (4.7-65.2) 7.6 (3.1-57.3) 62.9 (42.4-86.6) 0.006
Ferritin (ug/l) 596.5
(308.6-1087.6)
367.8
(174.7-522.0)
835.5
(635.4-1538.8) 0.015
SAA (mg/l) 134.4
(35.7-586.3)
46.9
(20.5-134.4)
607.1
(381.9-686.2) 0.003
C3 (g/l) 0.8 ± 0.2 0.8 ± 0.2 0.8 ± 0.1 0.389
C4 (g/l) 0.3 ± 0.1 0.3 ± 0.1 0.3 ± 0.1 0.426
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28
Figure legends 6
Figure 1. Kinetic analysis of cell counts of different populations of WBCs in 7
COVID-19 patients. 8
The absolute numbers of total WBCs (A), neutrophils (B), lymphocytes (C) and 9
monocytes (D) in the peripheral blood of mild (blue line) and severe (red line) 10
COVID-19 patients were analyzed at different time points after hospital admission. 11
Error bars, mean ± s.e.m.; *p<0.05. Results were test for significance with Two-way 12
ANOVA. 13
14
Figure 2. Kinetic analysis of cell counts of different lymphocyte subsets in 15
COVID-19 patients. 16
The absolute numbers of CD3+ T cells (A), CD8+ T cells (B), CD4+ T cells (C), B 17
cells (D) and NK cells (E) in the peripheral blood of mild (blue line) and severe (red 18
line) COVID-19 patients were analyzed at different time points after hospital 19
admission. Error bars, mean ± s.e.m.; *p<0.05. Results were test for significance with 20
Two-way ANOVA. 21
Figure 3. Kinetic analysis of levels of inflammatory cytokines the serum of 22
COVID-19 patients. 23
The concentrations of IL-6 (A), IL-10 (B), IL-2 (C), IL-4 (D), TNF-α (E) and IFN-γ 24
(F) in the serum of mild (blue line) and severe (red line) COVID-19 patients were 25
analyzed at different time points after hospital admission. Error bars, mean ± s.e.m.; 26
*p<0.05. Results were test for significance with Two-way ANOVA. 27
28
Figure 4. Prognostic factors of severe COVID-19. 29
(A) Principal component analysis was performed by R package “factoextra” to 30
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identify correlated variables for distinguishing severe patients from mild COVID-19 31
patients. Four mostly contributing variables, neutrophil-to-CD8+ T cell ratio (N8R), 32
neutrophil-to-lymphocyte ratio (NLR), neutrophil counts (NE) and White Blood Cells 33
counts (WBC) were identified. (B) ROC curve and AUC were calculated for these 4 34
selected parameters by using R package “pROC”. The further Logistic regression 35
analysis including the 4 factors of N8R>21.9, NLR>5.0, NE>3.2 and WBC>4.3 was 36
used to calculate OR. The results were obtained for NLR (OR:143, 95% 37
Cl:11.72-1745.3), N8R (OR:68.75, 95% Cl:8.55-552.68), NE (OR:22, 95% Cl: 38
3.646-132.735) and WBC (OR:55, 95% Cl:6.779-446.23). 39
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